Transfer learning methods adapt pre-trained models to learn new tasks or classify unseen data. This is crucial when limited labeled data is available, as training a large model from scratch (like CNNs in computer vision or transformers in NLP) with few samples often leads to overfitting. While these models may excel on test data, they often fail in real-world scenarios. Gathering sufficient data to prevent overfitting is a common bottleneck in model training.
Leveraging Pre-trained Models for Few-Shot Learning
Transfer learning offers a practical solution by utilizing the knowledge embedded within pre-trained models. A simple approach involves fine-tuning a pre-existing classification model for a new class using supervised learning with a limited number of labeled examples. More sophisticated techniques involve designing downstream tasks, often meta-learning tasks, for a model pre-trained via self-supervised pretext tasks. This is increasingly prevalent in NLP, especially with foundation models.
Adapting Neural Network Architectures for Efficient Learning
More complex transfer learning approaches modify the architecture of a trained neural network. For example, the outer layers responsible for final classification might be replaced or retrained, while preserving the internal layers responsible for feature extraction. Freezing or regularizing the weights of all but the outermost layers prevents “catastrophic forgetting” of previously learned knowledge during subsequent updates. This significantly accelerates learning in few-shot scenarios.
Relevance of Initial Training in Few-Shot Success
Transfer learning thrives when the initial training aligns with the new task. For instance, a model trained on specific bird species will generalize effectively to unseen bird species after fine-tuning with few labeled samples. This is because the learned weights of the CNN filters are already optimized for relevant features like plumage and beak size. Conversely, using few-shot learning to teach the same model to recognize vehicles will likely yield subpar results. The initial training on birds provides a strong foundation for related tasks, while recognizing vehicles requires significantly different feature extraction and knowledge. Therefore, the effectiveness of few-shot learning hinges on the relevance of the pre-trained model to the target task.
